**Architectural Logic**: Production Spark patterns require config, partitioning, and join optimization. ```python from pyspark.sql import SparkSession spark = SparkSession.builder.appName("ETL")\ .config("spark.sql.adaptive.enabled", "true")\ .getOrCreate() df1 =...
This hard-level SQL question appears frequently in data engineering interviews at companies like FedEx Dataworks. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, join, optimization) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity. The expert answer includes a code example that demonstrates the implementation pattern.
Architectural Logic: Production Spark patterns require config, partitioning, and join optimization.
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ETL")\
.config("spark.sql.adaptive.enabled", "true")\
.getOrCreate()
df1 = spark.read.option("header", True).csv("s3://bucket/orders.csv")
df2 = spark.read.option("header", True).csv("s3://bucket/customers.csv")
joined = df1.join(broadcast(df2), df1.customer_id == df2.id, "left")
joined.write.partitionBy("dt").mode("overwrite").parquet("s3://bucket/output/")
Why: Broadcast for small dims; partitionBy for downstream pruning; adaptive execution for skew. Scalability: Predicate pushdown; right-size parallelism. Cost: Avoid full scans; use incremental where possible.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.